First-year Computer Science Students Research Articles

Learning from Designing a Board Game for Policy Thinking in Computer Science

Technology does not exist in a vacuum, but is influenced by society and at the same time shaping it. In order to educate future computer scientists to be able to assess and create technology in a conscious and responsible manner, we provide first-year Computer Science (CS) students with an introductory course on different ways of thinking about CS by offering them a transdisciplinary view of the field. As we continuously strive to use engaging teaching methods to foster long-lasting interest for the topics of the course, as well as enhancing students’ learning experiences, we have introduced a unique game-based learning pipeline: In a game design course, advanced CS students design non-digital games that address various topics from the introductory course. These games are later played by first-year CS students, who are reflecting on the content and their experience as a part of their course assignments. In order to learn from the first iteration of this process, we analyse one of the games created, in particular its design and the educational objectives intended by the game designers, including the students’ feedback. We found that the game was perceived to be good in achieving learning outcomes; however, it scored even higher in terms of being an interesting and entertaining game. Overall, we conclude that the game was successful in creating an engaging learning experience, demonstrating the potential for game-based learning with non-digital games. This case study also highlights the importance of creating a suitable environment for game-based learning approaches to enhance the educational effects and utilise individual strengths of educational games, providing inspiration and groundwork for future research.

Research on the impact of the socio-educational environment on the academic performance of college students: the mediating role of study motivation.

Enhancing the academic performance (AP) of college students can contribute to the overall scientific literacy among this population, thereby fostering societal progress. The study investigates the correlation between college students' AP and the socio-educational environment (SEE, including family, roommates, and teachers), study motivation (SM, including self-efficacy and study behaviors). Based on the research findings, recommendations are offered to students, educators, and school administrators. Utilizing a stratified sampling approach, data was collected by selecting a sample of 330 first-year computer science students from a specific local university in Hebei Province, China. Data will be collected through a hierarchical sampling method. Using correlation analysis, difference analysis, and structural equation modeling (SEM) as data analysis methods. The data passed reliability and validity analysis (Cronbach's Alpha = 0.88, KMO = 0.88, χ2/df = 1.49RMSEA = 0.04). The independent sample T-test results showed that female students had higher academic performance than male students (p < 0.001), and there was no significant difference in academic performance between students from single parent or orphan families and students from normal families (p = 0.14), from non urban areas and from urban areas (p = 0.67). The results of the mediating effect analysis indicate that SM exerts complete mediation in the association between SEE and AP, with a mediating effect value of 0.18. The educational disparity between urban and rural areas in China is gradually narrowing. Support policies for students from impoverished families in higher education institutions are showing initial effectiveness. The conducive learning environment and educational atmosphere for students can indirectly influence their psychological state, thus impacting their academic performance during their university years.

Models of Data Structures in Educational Visualizations for Supporting Teaching and Learning Algorithms and Computer Programming

Teaching and learning computer programming is challenging for many undergraduate first-year computer science students. During introductory programming courses, novice programmers need to learn some basic algorithms, gain algorithmic thinking, improve their logical and problem-solving thinking skills, and learn data types, data structures, and the syntax of the chosen programming language. In literature, we can find various methods of teaching programming that can motivate students and reduce students’ cognitive load during the learning process of computer programming, e.g., using robotic kits, microcontrollers, microworld environments, virtual worlds, serious games, interactive animations, and visualizations. In this paper, we focus mainly on algorithm visualizations, especially on the different models of data structures that can be effectively used in educational visualizations. First, we show how a vector (one-dimensional array), a matrix (two-dimensional array), a singly linked list, and a graph can be represented by various models. Next, we also demonstrate some examples of interactive educational algorithm animations for teaching and learning elementary algorithms and some sorting algorithms, e.g., swapping two variables, summing elements of the array, mirroring the array, searching the minimum or maximum of the array, searching the index of minimum or maximum of the array, sorting elements of thearray using simple exchange sort, bubblesort, insertion sort, minsort, maxsort, quicksort, or mergesort. Finally, in the last part of the paper, we summarize our experiences in teaching algorithmization and computer programming using algorithm animations and visualizations and draw some conclusions.

Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs

This study investigated the extent to which self-report and digital-trace measures of students’ self-regulated learning in blended course designs align with each other amongst 145 first-year computer science students in a blended “computer systems” course. A self-reported Motivated Strategies for Learning Questionnaire was used to measure students’ self-efficacy, intrinsic motivation, test anxiety, and use of self-regulated learning strategies. Frequencies of interactions with six different online learning activities were digital-trace measures of students’ online learning interactions. Students’ course marks were used to represent their academic performance. SPSS 28 was used to analyse the data. A hierarchical cluster analysis using self-reported measures categorized students as better or poorer self-regulated learners; whereas a hierarchical cluster analysis using digital-trace measures clustered students as more active or less active online learners. One-way ANOVAs showed that: 1) better self-regulated learners had higher frequencies of interactions with three out of six online learning activities than poorer self-regulated learners. 2) More active online learners reported higher self-efficacy, higher intrinsic motivation, and more frequent use of positive self-regulated learning strategies, than less active online learners. Furthermore, a cross-tabulation showed significant (p < .01) but weak association between student clusters identified by self-reported and digital-trace measures, demonstrating self-reported and digital-trace descriptions of students’ self-regulated learning experiences were consistent to a limited extent. To help poorer self-regulated learners improve their learning experiences in blended course designs, teachers may invite better self-regulated learners to share how they approach learning in class.

Usability Evaluation of Imikode Virtual Reality Game to Facilitate Learning of Object-Oriented Programming

Many empirical studies have shown that educational games and recent technologies impact education and increase learning effectiveness, students’ motivation and engagement. The overall aim of this study is to evaluate the usability of Imikode, a virtual reality (VR) game that was developed to introduce the concepts of object-oriented programming to novices. The improved version of the Imikode VR game consists of three features: An artificial intelligence component designed to provide real-time error feedback to users, an intelligent agent that guides and teaches users how to play the game and finally, the integration of multiple game play that gives learners more opportunities to explore the VR environment for greater immersive learning experience. This study adopted a survey approach and recruited first-year computer science students to measure learner satisfaction with educational virtual reality games and examined the correlations among the attributes of the Usefulness, Satisfaction and Ease of Use questionnaire of usage of Imikode. The results showed that the students were satisfied with Imikode and perceived the virtual reality educational game as very useful for learning object-oriented programming concepts. In addition, there was a correlation among the questionnaire variables, which means that researchers can use the instrument for future usability studies in the context. We further proffered some design recommendations for building software tools.

On the computational thinking and diagrammatic reasoning of first-year computer science and engineering students

Computational thinking (CT) and diagrammatic reasoning (DR) are important competencies from the perspective of both Computer Science and Engineering education. CT is often described as a critically important attitude or skill set for all students regardless of the educational program in which they are enrolled. Diagrammatic reasoning is commonly referred to as a student's ability to think logically and solve complex problems. Accordingly, these two competencies are closely related and both skills are parallelly linked to several curriculum subjects (with preponderance in the case of STEM disciplines) during the educational process. Consequently, one might conclude that even without an explicit focus on them, students might develop these abilities latently as they advance with the K-12 current curriculum. We have proposed to test this assumption. In the experiment, 137 first-year students were involved in six different Computer Science and Engineering educational programs. Students were invited to participate in a CT and a DR test. We were particularly interested in possible correlations between the results of the two tests. Our results confirmed that computational thinking and diagrammatic reasoning are closely related abilities. We also found that CT, DR, and students' prior programming experience positively correlate with their first course exam results in Computer Science.

An Educational Setting to improve Students’ Understanding of Fundamental Computer Architecture Concepts

This paper presents an educational setting that attempts to enhance students’ understanding and facilitate students’ linking-inferencing skills. The proposed setting is structured in three stages. The first stage intends to explore students’ prior knowledge. The second stage aims to help students tackle their difficulties and misconceptions and deepen their understanding of the topics under study. This is attempted through individual student engagement in suitably-designed activities and relative feedback. As recorded in previous research, students’ difficulties feedback on the material development. The third stage of the educational setting exploits social interaction to help students reorganize their knowledge of the concepts under study. The web-based application of the proposed educational setting indicated improvement in first-year Computer Science (CS) students’ understanding of fundamental Computer Architecture concepts and progress in students’ linking-inference skills. These results encourage integration in the instructional process of interventions designed according to the proposed setting in order to support and enhance students’ understanding of troublesome concepts and their interrelations.

Responsible Computing: A Longitudinal Study of a Peer-led Ethics Learning Framework

We studied the impact of introducing first-year computer science (CS) students to ethical thinking about the social justice impacts of data collection, tracking, bias, internet privacy, and competitive “real world” system design and critique activities. While basic content was consistent for all, one group was involved throughout the course in peer discussions designed to foster greater engagement, with the anticipation that this would enable students to reach new levels of sensitivity through peer-to-peer interaction. This article reports on our observation of this design, interview, and project data collected throughout the course as well interviews conducted eight months later to learn about how students were retaining and applying what they learned. We found that students are sensitive to the technology-related risks and vulnerabilities encountered by individuals based on race, gender, and, to some extent, age, but they struggle to assess who is responsible for these risks, what to do about bias in technology design, and how to mitigate harms for individuals whom they perceive to be vulnerable, furthering the argument for an integrated ethics curriculum. We explore the value of formal peer-led discussion to evolve social justice thinking with a focus on identity, though note that opportunities for any group discussion are meaningful to students’ thinking about social justice. Over the longer term, students tend to recall and apply ethics that is closely related to their identity, suggesting that empathy has limits.

Combining diagnostic testing and student mentorship to increase engagement and progression of first-year computer science students

ABSTRACT Computer Science (CS) degrees have some of the poorest continuation rates across HE. This study describes an intervention within a diverse CS student cohort to identify students who may be at risk of mathematical academic failure and the success of student mentor-led workshops in enhancing these students’ mathematical ability. Diagnostic screening identified 46% (N = 92) of the cohort with low mathematical knowledge, with prior entry qualification being the only significant (p < 0.0001) reason for this gap. These identified students were invited to attend weekly mathematical workshops, with a positive correlation between attendance and attainment and females more likely to attend. The CS students were satisfied with having the mathematical workshops delivered by student mentors. In summary, this combined intervention of screening and mentor-led academic support successfully identified gaps within first-year students’ mathematical knowledge, with the workshops addressing the diversity in mathematical understanding and helping reduce a prior qualification awarding gap.

Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

The Role of Pre-writing Strategies to Enhance the Students’ Idea Generating Abilities: The Case of First-Year Computer Science Students of Haramaya University

This research was aimed at fostering students’ idea generating abilities via the implementation of pre-writing strategies. The study adopted an action research design. The first phase involved problem identification and causes of the problems through focus group discussion and classroom observation. After identifying the challenges, systematically planned actions were implemented for eight successive weeks. During the intervention phase, awareness was created among the students on the importance of using idea-generating strategies before starting to write a text. As a result, four idea-generating strategies were employed; brainstorming, clustering, free-writing and questioning. Finally, the results of the actions taken were evaluated via observation, questionnaire, and focus group discussion. The findings reveal that the students showed interest in using idea-generating strategies, and the strategies used helped them to come up with adequate ideas in order to develop a text. The strategy also helped them to think exhaustively about what to write and how to support their argument before starting writing the actual text. Out of the four strategies employed, brainstorming was found to be the most convenient strategy to generate ideas. The strategies used were found to prevent students from unnecessary pen pause and frequent deletion of what they produced. Therefore, it is possible to comprehend from this action research that using idea-generating strategies will ease the practice of developing a text.This research was aimed at fostering students’ idea generating abilities via the implementationof pre-writing strategies. The study adopted an action research design. The first phase involvedproblem identification and causes of the problems through focus group discussion and classroomobservation. After identifying the challenges, systematically planned actions were implementedfor eight successive weeks. During the intervention phase, awareness was created among thestudents on the importance of using idea-generating strategies before starting to write a text.As a result, four idea-generating strategies were employed; brainstorming, clustering, freewritingand questioning. Finally, the results of the actions taken were evaluated via observation,questionnaire, and focus group discussion. The findings reveal that the students showed interestin using idea-generating strategies, and the strategies used helped them to come up with adequateideas in order to develop a text. The strategy also helped them to think exhaustively about whatto write and how to support their argument before starting writing the actual text. Out of the fourstrategies employed, brainstorming was found to be the most convenient strategy to generateideas. The strategies used were found to prevent students from unnecessary pen pause andfrequent deletion of what they produced. Therefore, it is possible to comprehend from this actionresearch that using idea-generating strategies will ease the practice of developing a text.

Rethinking how to teach programming to newcomers

The Communications Web site, http://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM http://cacm.acm.org/blogs/blog-cacm Judy Robertson reviews what happened when she decided to introduce Android development into the curriculum for first-year computer science students.

Using model checkers in an introductory course on operating systems

During the last three years, we have been experimenting with the use of the Uppaal model checker in an introductory course on operating systems for first-year Computer Science students at the Radboud University Nijmegen. The course uses model checkers as a tool to explain, visualize and solve concurrency problems. Our experience is that students enjoy to play with model checkers because it makes concurrency issues tangible. Even though it is hard to measure objectively, we think that model checkers really help students to obtain a deeper insight into concurrency. In this article, we report on our experiences in the classroom, explain how mutual exclusion algorithms, semaphores and monitors can conveniently be modeled in Uppaal, and present some results on properties of small, concurrent patterns.

Towards an integrated learning laboratory environment for first-year computer science students

This paper describes an evolving attempt to provide an integrated learning laboratory environment (ILLE) to enhance the learning goals and strategies for first-year computer science students using Java as a first language. The proposed ILLE models a real computer-learning laboratory in which instructors walk around watching and helping students on request. In essence, the system consists of a Java development environment centered round a live communicator with a facility for an instructor to focus and work on the desk top of a particular student. The system is written in 4000 lines of Java and runs in exactly the same way on Windows, UNIX and MacOS. The ILLE gathers usage information that is being used to improve the learning for first year students. It has been used twice with large first year classes. Examples show how extracts from dialogue files enhanced the ILLE.

The Software Process: A Parallel Approach through Problem Solving and Program Development

First-year computer science students are receiving early introduction to the software process through a problem solving and program development approach. We present a methodology that addresses the needs and difficulties of students learning programming, incorporating the tasks required for solving problems and writing programs. This approach allows for incremental exposure to the complex field of software engineering, consistent with the level of the introductory computing course, while providing the learner with fundamental skills applicable to other domains. The correlation between the software process and the problem solving/program development approach is also demonstrated.

Providing intellectual focus to CS1/CS2

First-year computer science students need to see clearly that computer science as a discipline has an important intellectual role to play and that it offers deep philosophical questions, much like the other hard sciences and mathematics; that CS is not "just programming". An appropriate intellectual focus for CS1/CS2 can be built on the foundations of systems thinking and mathematical modeling, as these principles are manifested in a component-based software paradigm. We outline some of the main technical features of this approach to CS1/CS2 and report preliminary observations from our experience with it.

The first course in computer science needs a mathematics corequisite

First-year computer science students need to know and use a considerable amount of mathematics. A corequired course in discrete mathematics is a good solution.